Aside

Programming

R / tidyverse / tidymodels
Python
git / GitHub
C++
Bash
HTML
css

Data analysis

Exploratory data analysis
Data visualization (e.g. ggplot2)
Data cleaning (e.g. dplyr, pandas)
Deep Learning (Keras, TF)
Machine learning (e.g. Factor Analysis, GLMs, SVMs, Tree-based models)

Literate Coding

R Markdown
Quarto
Jupyter Notebooks
LaTex
flexdashboards
blogdown / bookdown

Disclaimer

Resume generated in R with pagedown

Source code: github.com/keyes-timothy/cv

Updated December 04, 2022.

🏳️‍🌈

Main

Timothy Keyes

I am a data scientist, bioinformatician, and cancer biologist. In my work, I develop statistical and machine learning algorithms for analyzing high-dimensional single-cell data and predicting clinical outcomes in cancer patients.

I am searching for a position at the intersection of biomedical data science, machine learning, and medicine where I can use data to solve problems relevant to human health.

Education

M.D./Ph.D. - Cancer Biology

Stanford University

Stanford, CA

Current - 2015

  • National Cancer Institute National Research Service Award fellow
  • Advisors: Kara Davis and Garry Nolan

M.S. - Biomedical Informatics (concurrent with MD/PhD)

Stanford University

Stanford, CA

Current - 2020

B.A. - Psychology and Computational Neuroscience

Princeton University

Princeton, NJ

2014 - 2010

  • Summa cum laude
  • GPA: 3.99

Select Employment

Data Science Mentor - Posit Academy

Posit, PBC (formerly RStudio, PBC)

Stanford, CA

Current - 2022

  • Leading group-based instruction and one-on-one mentoring for Posit Academy cohorts learning R and Python
  • Engaging in regular professional development programming with experienced data science educators

Graduate Intern - Oncology Bioinformatics, gRED

Genentech, Inc

South San Francisco, CA

2022

  • Codeveloped a novel algorithm for detecting transcription factor network perturbations in cancer using Bayesian network modeling
  • Automated a multiomic data integration pipeline for ATAC- and RNA-seq

Select Publications

{tidytof}: A user-friendly framework for scalable and reproducible high-dimensional cytometry data analysis.

Under review (copy available upon request)

N/A

2022

CytofIn enables Integrated Analysis of Public Mass Cytometry Datasets using Generalized Anchors

Nature Communications

N/A

2022

  • Lo YC, Keyes TJ, Jager A, Sarno J, Domizi P, Majeti R, Sakamoto KM, Lacayo N, Mulligan CG, Waters J, Sahaf B, Bendall SC, Davis KL

A cancer biologist's primer on machine learning applications in high-dimensional cytometry

Cytometry

N/A

2020

  • Keyes TJ, Domizi P, Lo YC, Nolan GP, and Davis KL